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A comparison of named entity recognition tools applied to biographical texts
Abstract Named entity recognition (NER) is a populaNamed entity recognition (NER) is a popular domain of natural language processing. For this reason, many tools exist to perform this task. Amongst other points, they differ in the processing method they rely upon, the entity types they can detect, the nature of the text they can handle, and their input/output formats. This makes it difficult for a user to select an appropriate NER tool for a specific situation. In this article, we try to answer this question in the context of biographic texts. For this matter, we first constitute a new corpus by annotating 247 Wikipedia articles. We then select 4 publicly available, well known and free for research NER tools for comparison: Stanford NER, Illinois NET, OpenCalais NER WS and Alias-i LingPipe. We apply them to our corpus, assess their performances and compare them. When considering overall performances, a clear hierarchy emerges: Stanford has the best results, followed by LingPipe, Illionois and OpenCalais. However, a more detailed evaluation performed relatively to entity types and article categories highlights the fact their performances are diversely influenced by those factors. This complementarity opens an interesting perspective regarding the combination of these individual tools in order to improve performance.ual tools in order to improve performance.
Abstractsub Named entity recognition (NER) is a populaNamed entity recognition (NER) is a popular domain of natural language processing. For this reason, many tools exist to perform this task. Amongst other points, they differ in the processing method they rely upon, the entity types they can detect, the nature of the text they can handle, and their input/output formats. This makes it difficult for a user to select an appropriate NER tool for a specific situation. In this article, we try to answer this question in the context of biographic texts. For this matter, we first constitute a new corpus by annotating 247 Wikipedia articles. We then select 4 publicly available, well known and free for research NER tools for comparison: Stanford NER, Illinois NET, OpenCalais NER WS and Alias-i LingPipe. We apply them to our corpus, assess their performances and compare them. When considering overall performances, a clear hierarchy emerges: Stanford has the best results, followed by LingPipe, Illionois and OpenCalais. However, a more detailed evaluation performed relatively to entity types and article categories highlights the fact their performances are diversely influenced by those factors. This complementarity opens an interesting perspective regarding the combination of these individual tools in order to improve performance.ual tools in order to improve performance.
Bibtextype inproceedings  +
Doi 10.1109/IcConSCS.2013.6632052  +
Has author Atdag S. + , Labatut V. +
Has extra keyword Entity-types + , Illinois + , Improve performance + , Input/output + , Named entity recognition + , NAtural language processing + , Processing method + , Wikipedia articles + , Computer science + , Linguistics + , Natural language processing systems + , Tool +
Isbn 9781479920204  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 228–233  +
Published in 2013 2nd International Conference on Systems and Computer Science, ICSCS 2013 +
Title A comparison of named entity recognition tools applied to biographical texts +
Type conference paper  +
Year 2013 +
Creation dateThis property is a special property in this wiki. 6 November 2014 17:09:58  +
Categories Publications without keywords parameter  + , Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Conference papers  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 6 November 2014 17:09:58  +
DateThis property is a special property in this wiki. 2013  +
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